What’s in a role: Liying Wang, VP of AI Training Operations at x.ai

By: Amanda Mulay

Every day, in my role as senior talent manager at Lerer Hippeau, I talk with our founders to learn about their hiring needs and challenges. During these conversations I get to know what they need to grow their teams, learn the ins and outs of the company’s structure, and hear about their stars and emerging leaders. In this new series, “What’s in a role,” I’ll interview individuals holding unique positions across our portfolio, responsible for propelling crucial initiatives core to the function of the business.

Kicking off this series is Liying Wang, VP of AI Training at x.ai. From how she got started in AI to her perspective on the future of automation, here’s a look at her day-to-day at x.ai, best known as the creator of AI scheduling assistants Amy and Andrew Ingram.

Amanda Mulay: Where did you start your career and how did you end up in your current role at X.ai?

Liying Wang: I grew up in Palo Alto so tech is part of my constitution. I studied Existentialism and Literary Theory at Cornell, both of which had a profound impact on the way I view the world and have given me the tools to approach this linguistics problem that is one of the biggest challenges within my role. I think having a humanities background has been invaluable to my success in my job today.

I joined Lyft in the early days way back when it was still Zimride, and have been working in tech ever since. The possibilities to change the world in a positive and disruptive way are unlike any other industry. After Lyft, I tried my hand at my own startup, an e-commerce business for sports nutrition, which was an amazing growing experience.

Then I joined Facebook in Menlo Park, where I ran the human annotator side of various machine learning products. The breadth of types of products and brilliant product and engineering partners gave me the opportunity to learn how to evaluate, optimize and scale data annotation.

I heard about the role at New York-based x.ai through a Crunchbase notification. The company had just raised a Series B and I thought it was a super interesting opportunity and knew I wanted to stay in the AI/machine learning industry. I reached out cold and heard from Dennis [Mortensen] within a day. The rest is history!

Mulay: What does your role entail and how do you typically explain it?

Wang: My job is to solve complex problems in unchartered territory, and I wouldn’t have it any other way! My domain expertise is in building, running, and optimizing our AI Training team. The data annotations produced by this team provide the inputs for our machines to learn how to understand natural language in the production environment.

I designed the infrastructure used by the AI Trainers to annotate data, created the annotation quality controls, processes, feedback mechanisms, and guidelines to teach the AI Trainers how to achieve high quality data annotations. Natural language is fluid and constantly evolving, and all of these components require perpetual flexibility and optimization.

At a high level, what happens is this: if the machine has low confidence in its parsing of an email that Amy or Andrew receives, our system sends the AI Trainer team a data annotation task. This data annotation task will then feed back into the machine’s learning so it can handle it with higher confidence in the future. This process happens for specific micro tasks, so humans don’t ever decide how Amy will respond to a given email or get involved in the actual scheduling in any way.

The data produced by the AI Training team must align with the machine learning models in addition to the product’s functional requirements. The inextricably linked systems require constant collaboration and fine tuning in order to work in harmony.

The way I sometimes explain it to people is to imagine you have a baby robot, and you have to teach the baby robot to perform independent tasks on its own. In order to do that, you need to provide it loads of consistent inputs so it can learn, while accounting for other variables.

Mulay: What are the most common areas of confusion when you’re describing your role in AI?

Wang: I see AI as being able to create opportunity for mankind rather than take opportunity away. Unfortunately the latter is something I hear more frequently than the former. I see this occurring in two general ways.

1.) Being able to take over a vast variety of tasks which allows people to focus on more challenging, creative ones that AI does not have the cognitive capability of performing. Providing people time to channel their effort and energy towards more intricate and complicated problem-solving is highly beneficial. I certainly see x.ai as providing people increased efficiency, so instead of having to deal with the mundane task of going back and forth to schedule meetings, they can redirect their attention and focus on more important things.

2.) Disrupting industries for the betterment of humanity. AI can (and is starting to) make leaps and bounds towards revolutionizing how we approach other areas such as healthcare or transportation.

While most of the misperceptions I hear are about AI itself, in terms of my role, most people are surprised I don’t have an engineering or programming background. The area around AI that is relatively less known is the training part which for me dealing with natural language processing, is basically a linguistics challenge. That’s why my background in Literary Theory, Deconstruction, and Operations align well.

Mulay: What do you find the most challenging about your role or responsibilities?

Wang: What drives me is being able to capture the limitless aspects of language with all of its subtleties/variations/unique eccentricities to fulfill data science and product requirements while achieving a level of quality so high we are striving to hit the ceiling of human performance.

Mulay: How do you see your role and professional skills growing from here?

Wang: AI is such a dynamic and growing field, so the opportunities are really endless. Whether that’s teaching Amy to learn different types of tasks, adding new features, expanding to other platforms to interact with Amy, or training Amy to understand other languages — all of these opportunities will require me to expand my current role and adjust for the nuances required for each new linguistic challenge.

AI is going to keep growing and changing. It’s an industry that’s rapidly expanding right now and there’s AI everywhere we look. I think about AI as training machines by providing inputs that help teach them to perform tasks on their own. Every industry is trying to become more efficient, so the need for AI is omnipresent.

Mulay: How is this role unique to x.ai?

Wang: The data I’m training is specific to Amy, but the approach, philosophy and tenets are certainly transposable to different needs and products.

Mulay: What makes you most excited about working this area?

Wang: I love working within a space that is still relatively new. The uncertainty is invigorating, and I love tackling these big hairy problems and having to problem solve and iterate as we go.

Interested in a role at x.ai? Check out current job openings here.

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